Biostatistics Seminar Series: Brent A. Johnson, PhD

Associate Professor of Biostatistics Department of Biostatistics and Computational Biology University of Rochester Abstract:  Occasionally, investigators collect auxiliary marks at the time of failure in a clinical study. Because the failure event may be censored at the end of the follow-up period, these marked endpoints are subject to induced censoring.  We propose two new families of two-sample tests for the null hypothesis of no difference in mark-scale distribution that allows for arbitrary associations between mark and time. One family of proposed tests is a nonparametric extension of an existing semi-parametric linear test of the same null hypothesis while a second family of tests is based on a novel marked rank process. Simulation studies indicate that the proposed tests have the desired size and possess adequate statistical power to reject the null hypothesis under a simple change of location in the marginal mark distribution.  When the marginal mark distribution has heavy tails, the proposed rank-based tests can be nearly twice as powerful as linear tests.